AI/ML Infrastructure Engineer – AWS Trainium

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We are hiring an experienced AWS Trainium / AI Infrastructure Engineer to support large-scale AI/ML infrastructure and distributed model training workloads in AWS.
Role: AWS Trainium / AI Infrastructure Engineer
Location: USA – Remote
Start Date: Immediate
Background Check: Mandatory
Key Responsibilities
Design, deploy, and support scalable AI/ML infrastructure on AWS.
Build and optimize distributed training and inference workloads using AWS Trainium/Inferentia.
Develop and optimize AI workloads using the AWS Neuron SDK.
Support LLM, Generative AI, and large-scale machine learning workloads.
Deploy AI workloads using Amazon EKS/Kubernetes, Docker, Python, and PyTorch.
Benchmark and optimize AI model performance across GPU and AWS AI accelerator environments.
Support migration of GPU-based workloads to AWS Trainium/Inferentia.
Collaborate with ML engineers and infrastructure teams to improve scalability, reliability, and performance.
Required Skills
5+ years of experience in Cloud, Data, AI Infrastructure, or a related field.
Hands-on experience with AWS Trainium, Inferentia, EC2 Trn instances, and AWS Neuron SDK.
Strong knowledge of LLMs, Generative AI, distributed training, and AI/ML infrastructure.
Experience with AWS, EKS/Kubernetes, Docker, Python, and PyTorch.
Experience supporting large-scale AI/ML training workloads in AWS.
Strong understanding of AI performance optimization, benchmarking, and cloud-native architectures.
Experience migrating or optimizing GPU-based AI workloads for AWS accelerators is highly preferred.
Interested candidates: Please share your updated resume along with your location, work authorization, and availability.

Work arrangement
Yes